{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Historical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section illustrates how to retrieve **historical data** for different instruments.\n",
    "\n",
    "First, the API connection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import fxcmpy\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "con = fxcmpy.fxcmpy(config_file='fxcm.cfg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Available Instruments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list of **instruments** for which historical data is available is returned by `con. get_instruments_for_candles()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EUR/USD', 'USD/JPY', 'GBP/USD', 'USD/CHF']\n",
      "['EUR/CHF', 'AUD/USD', 'USD/CAD', 'NZD/USD']\n",
      "['EUR/GBP', 'EUR/JPY', 'GBP/JPY', 'AUD/JPY']\n",
      "['GBP/AUD', 'USD/CNH', 'FRA40', 'GER30']\n",
      "['UK100', 'US30', 'USDOLLAR', 'XAU/USD']\n",
      "['XAG/USD']\n"
     ]
    }
   ],
   "source": [
    "instruments = con.get_instruments_for_candles()\n",
    "for i in range(int(len(instruments)/4)):\n",
    "    print(instruments[i*4:(i+1)*4])\n",
    "print(instruments[(i+1)*4:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetching the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a simple case, the `con.get_candles()` method returns the most recent data points available for a specified `instrument` and `period` value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
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       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-21 22:00:00</th>\n",
       "      <td>113.374</td>\n",
       "      <td>113.312</td>\n",
       "      <td>113.628</td>\n",
       "      <td>113.189</td>\n",
       "      <td>113.422</td>\n",
       "      <td>113.348</td>\n",
       "      <td>113.649</td>\n",
       "      <td>113.211</td>\n",
       "      <td>188634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-22 22:00:00</th>\n",
       "      <td>113.311</td>\n",
       "      <td>113.254</td>\n",
       "      <td>113.437</td>\n",
       "      <td>113.230</td>\n",
       "      <td>113.347</td>\n",
       "      <td>113.352</td>\n",
       "      <td>113.457</td>\n",
       "      <td>113.264</td>\n",
       "      <td>174379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27 22:00:00</th>\n",
       "      <td>113.299</td>\n",
       "      <td>113.332</td>\n",
       "      <td>113.341</td>\n",
       "      <td>113.211</td>\n",
       "      <td>113.320</td>\n",
       "      <td>113.368</td>\n",
       "      <td>113.393</td>\n",
       "      <td>113.232</td>\n",
       "      <td>46062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28 22:00:00</th>\n",
       "      <td>113.332</td>\n",
       "      <td>112.843</td>\n",
       "      <td>113.334</td>\n",
       "      <td>112.652</td>\n",
       "      <td>113.368</td>\n",
       "      <td>112.897</td>\n",
       "      <td>113.372</td>\n",
       "      <td>112.674</td>\n",
       "      <td>234357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29 22:00:00</th>\n",
       "      <td>112.843</td>\n",
       "      <td>112.641</td>\n",
       "      <td>112.959</td>\n",
       "      <td>112.462</td>\n",
       "      <td>112.897</td>\n",
       "      <td>112.739</td>\n",
       "      <td>112.982</td>\n",
       "      <td>112.484</td>\n",
       "      <td>195779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 22:00:00</th>\n",
       "      <td>112.638</td>\n",
       "      <td>112.586</td>\n",
       "      <td>112.638</td>\n",
       "      <td>112.584</td>\n",
       "      <td>112.732</td>\n",
       "      <td>112.660</td>\n",
       "      <td>112.732</td>\n",
       "      <td>112.660</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02 22:00:00</th>\n",
       "      <td>112.586</td>\n",
       "      <td>112.263</td>\n",
       "      <td>112.782</td>\n",
       "      <td>112.045</td>\n",
       "      <td>112.660</td>\n",
       "      <td>112.317</td>\n",
       "      <td>112.804</td>\n",
       "      <td>112.066</td>\n",
       "      <td>176506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03 22:00:00</th>\n",
       "      <td>112.263</td>\n",
       "      <td>112.492</td>\n",
       "      <td>112.597</td>\n",
       "      <td>112.162</td>\n",
       "      <td>112.316</td>\n",
       "      <td>112.528</td>\n",
       "      <td>112.620</td>\n",
       "      <td>112.183</td>\n",
       "      <td>174543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04 22:00:00</th>\n",
       "      <td>112.492</td>\n",
       "      <td>112.732</td>\n",
       "      <td>112.854</td>\n",
       "      <td>112.451</td>\n",
       "      <td>112.528</td>\n",
       "      <td>112.768</td>\n",
       "      <td>112.876</td>\n",
       "      <td>112.492</td>\n",
       "      <td>204606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05 22:00:00</th>\n",
       "      <td>112.732</td>\n",
       "      <td>113.008</td>\n",
       "      <td>113.293</td>\n",
       "      <td>112.707</td>\n",
       "      <td>112.768</td>\n",
       "      <td>113.108</td>\n",
       "      <td>113.337</td>\n",
       "      <td>112.736</td>\n",
       "      <td>197294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2017-12-21 22:00:00  113.374   113.312  113.628  113.189  113.422   113.348   \n",
       "2017-12-22 22:00:00  113.311   113.254  113.437  113.230  113.347   113.352   \n",
       "2017-12-27 22:00:00  113.299   113.332  113.341  113.211  113.320   113.368   \n",
       "2017-12-28 22:00:00  113.332   112.843  113.334  112.652  113.368   112.897   \n",
       "2017-12-29 22:00:00  112.843   112.641  112.959  112.462  112.897   112.739   \n",
       "2018-01-01 22:00:00  112.638   112.586  112.638  112.584  112.732   112.660   \n",
       "2018-01-02 22:00:00  112.586   112.263  112.782  112.045  112.660   112.317   \n",
       "2018-01-03 22:00:00  112.263   112.492  112.597  112.162  112.316   112.528   \n",
       "2018-01-04 22:00:00  112.492   112.732  112.854  112.451  112.528   112.768   \n",
       "2018-01-05 22:00:00  112.732   113.008  113.293  112.707  112.768   113.108   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2017-12-21 22:00:00  113.649  113.211   188634  \n",
       "2017-12-22 22:00:00  113.457  113.264   174379  \n",
       "2017-12-27 22:00:00  113.393  113.232    46062  \n",
       "2017-12-28 22:00:00  113.372  112.674   234357  \n",
       "2017-12-29 22:00:00  112.982  112.484   195779  \n",
       "2018-01-01 22:00:00  112.732  112.660        3  \n",
       "2018-01-02 22:00:00  112.804  112.066   176506  \n",
       "2018-01-03 22:00:00  112.620  112.183   174543  \n",
       "2018-01-04 22:00:00  112.876  112.492   204606  \n",
       "2018-01-05 22:00:00  113.337  112.736   197294  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_candles('USD/JPY', period='D1')  # daily data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the method returns a `pandas` `DataFrame` object which simplifies the majority of typical analytics and visualizations tasks significantly (see http://pandas.pydata.org)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter `period` defines the frequency of the data to be retrieved. Below is a list of all currently available frequencies.\n",
    "\n",
    "* minutes: `m1`, `m5`, `m15` and `m30`,\n",
    "* hours: `H1`, `H2`, `H3`, `H4`, `H6` and `H8`,\n",
    "* one day: `D1`,\n",
    "* one week: `W1`,\n",
    "* one month: `M1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, `con.get_candles()` returns data for the last 10 available periods, depending on the parameter value for `period`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>asklow</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-31 22:00:00</th>\n",
       "      <td>1.07960</td>\n",
       "      <td>1.05751</td>\n",
       "      <td>1.08277</td>\n",
       "      <td>1.04925</td>\n",
       "      <td>1.07994</td>\n",
       "      <td>1.05797</td>\n",
       "      <td>1.08301</td>\n",
       "      <td>1.04948</td>\n",
       "      <td>5784569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-28 21:00:00</th>\n",
       "      <td>1.05751</td>\n",
       "      <td>1.06557</td>\n",
       "      <td>1.09047</td>\n",
       "      <td>1.04937</td>\n",
       "      <td>1.05797</td>\n",
       "      <td>1.06612</td>\n",
       "      <td>1.09077</td>\n",
       "      <td>1.04961</td>\n",
       "      <td>6756270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31 21:00:00</th>\n",
       "      <td>1.06557</td>\n",
       "      <td>1.09093</td>\n",
       "      <td>1.09498</td>\n",
       "      <td>1.05683</td>\n",
       "      <td>1.06612</td>\n",
       "      <td>1.09184</td>\n",
       "      <td>1.09521</td>\n",
       "      <td>1.05711</td>\n",
       "      <td>4077696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-30 21:00:00</th>\n",
       "      <td>1.09093</td>\n",
       "      <td>1.12412</td>\n",
       "      <td>1.12669</td>\n",
       "      <td>1.08380</td>\n",
       "      <td>1.09184</td>\n",
       "      <td>1.12440</td>\n",
       "      <td>1.12704</td>\n",
       "      <td>1.08403</td>\n",
       "      <td>5554385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-31 21:00:00</th>\n",
       "      <td>1.12412</td>\n",
       "      <td>1.14162</td>\n",
       "      <td>1.14445</td>\n",
       "      <td>1.11178</td>\n",
       "      <td>1.12440</td>\n",
       "      <td>1.14254</td>\n",
       "      <td>1.14467</td>\n",
       "      <td>1.11201</td>\n",
       "      <td>5851248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30 21:00:00</th>\n",
       "      <td>1.14162</td>\n",
       "      <td>1.18403</td>\n",
       "      <td>1.18444</td>\n",
       "      <td>1.13112</td>\n",
       "      <td>1.14254</td>\n",
       "      <td>1.18428</td>\n",
       "      <td>1.18473</td>\n",
       "      <td>1.13137</td>\n",
       "      <td>5946183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-31 21:00:00</th>\n",
       "      <td>1.18403</td>\n",
       "      <td>1.19081</td>\n",
       "      <td>1.20693</td>\n",
       "      <td>1.16610</td>\n",
       "      <td>1.18428</td>\n",
       "      <td>1.19110</td>\n",
       "      <td>1.20716</td>\n",
       "      <td>1.16634</td>\n",
       "      <td>6893224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 21:00:00</th>\n",
       "      <td>1.19081</td>\n",
       "      <td>1.18108</td>\n",
       "      <td>1.20913</td>\n",
       "      <td>1.17160</td>\n",
       "      <td>1.19110</td>\n",
       "      <td>1.18197</td>\n",
       "      <td>1.20937</td>\n",
       "      <td>1.17181</td>\n",
       "      <td>6183348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30 21:00:00</th>\n",
       "      <td>1.18108</td>\n",
       "      <td>1.16450</td>\n",
       "      <td>1.18792</td>\n",
       "      <td>1.15731</td>\n",
       "      <td>1.18197</td>\n",
       "      <td>1.16476</td>\n",
       "      <td>1.18810</td>\n",
       "      <td>1.15750</td>\n",
       "      <td>4598301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 21:00:00</th>\n",
       "      <td>1.19981</td>\n",
       "      <td>1.20087</td>\n",
       "      <td>1.20110</td>\n",
       "      <td>1.19917</td>\n",
       "      <td>1.20007</td>\n",
       "      <td>1.20161</td>\n",
       "      <td>1.20161</td>\n",
       "      <td>1.20005</td>\n",
       "      <td>86</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2017-01-31 22:00:00  1.07960   1.05751  1.08277  1.04925  1.07994   1.05797   \n",
       "2017-02-28 21:00:00  1.05751   1.06557  1.09047  1.04937  1.05797   1.06612   \n",
       "2017-03-31 21:00:00  1.06557   1.09093  1.09498  1.05683  1.06612   1.09184   \n",
       "2017-04-30 21:00:00  1.09093   1.12412  1.12669  1.08380  1.09184   1.12440   \n",
       "2017-05-31 21:00:00  1.12412   1.14162  1.14445  1.11178  1.12440   1.14254   \n",
       "2017-06-30 21:00:00  1.14162   1.18403  1.18444  1.13112  1.14254   1.18428   \n",
       "2017-07-31 21:00:00  1.18403   1.19081  1.20693  1.16610  1.18428   1.19110   \n",
       "2017-08-31 21:00:00  1.19081   1.18108  1.20913  1.17160  1.19110   1.18197   \n",
       "2017-09-30 21:00:00  1.18108   1.16450  1.18792  1.15731  1.18197   1.16476   \n",
       "2018-01-01 21:00:00  1.19981   1.20087  1.20110  1.19917  1.20007   1.20161   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2017-01-31 22:00:00  1.08301  1.04948  5784569  \n",
       "2017-02-28 21:00:00  1.09077  1.04961  6756270  \n",
       "2017-03-31 21:00:00  1.09521  1.05711  4077696  \n",
       "2017-04-30 21:00:00  1.12704  1.08403  5554385  \n",
       "2017-05-31 21:00:00  1.14467  1.11201  5851248  \n",
       "2017-06-30 21:00:00  1.18473  1.13137  5946183  \n",
       "2017-07-31 21:00:00  1.20716  1.16634  6893224  \n",
       "2017-08-31 21:00:00  1.20937  1.17181  6183348  \n",
       "2017-09-30 21:00:00  1.18810  1.15750  4598301  \n",
       "2018-01-01 21:00:00  1.20161  1.20005       86  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_candles('EUR/USD', period='M1')  # monthly data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A number different from 10 can also be defined via the `number` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
       "      <th>askopen</th>\n",
       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-05 21:55:00</th>\n",
       "      <td>1.20276</td>\n",
       "      <td>1.20280</td>\n",
       "      <td>1.20285</td>\n",
       "      <td>1.20269</td>\n",
       "      <td>1.20307</td>\n",
       "      <td>1.20312</td>\n",
       "      <td>1.20317</td>\n",
       "      <td>1.20302</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05 21:56:00</th>\n",
       "      <td>1.20280</td>\n",
       "      <td>1.20275</td>\n",
       "      <td>1.20283</td>\n",
       "      <td>1.20268</td>\n",
       "      <td>1.20312</td>\n",
       "      <td>1.20312</td>\n",
       "      <td>1.20326</td>\n",
       "      <td>1.20307</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05 21:57:00</th>\n",
       "      <td>1.20275</td>\n",
       "      <td>1.20280</td>\n",
       "      <td>1.20282</td>\n",
       "      <td>1.20275</td>\n",
       "      <td>1.20312</td>\n",
       "      <td>1.20317</td>\n",
       "      <td>1.20319</td>\n",
       "      <td>1.20312</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05 21:58:00</th>\n",
       "      <td>1.20280</td>\n",
       "      <td>1.20293</td>\n",
       "      <td>1.20297</td>\n",
       "      <td>1.20280</td>\n",
       "      <td>1.20317</td>\n",
       "      <td>1.20327</td>\n",
       "      <td>1.20329</td>\n",
       "      <td>1.20314</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05 21:59:00</th>\n",
       "      <td>1.20293</td>\n",
       "      <td>1.20263</td>\n",
       "      <td>1.20297</td>\n",
       "      <td>1.20263</td>\n",
       "      <td>1.20327</td>\n",
       "      <td>1.20363</td>\n",
       "      <td>1.20363</td>\n",
       "      <td>1.20327</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2018-01-05 21:55:00  1.20276   1.20280  1.20285  1.20269  1.20307   1.20312   \n",
       "2018-01-05 21:56:00  1.20280   1.20275  1.20283  1.20268  1.20312   1.20312   \n",
       "2018-01-05 21:57:00  1.20275   1.20280  1.20282  1.20275  1.20312   1.20317   \n",
       "2018-01-05 21:58:00  1.20280   1.20293  1.20297  1.20280  1.20317   1.20327   \n",
       "2018-01-05 21:59:00  1.20293   1.20263  1.20297  1.20263  1.20327   1.20363   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2018-01-05 21:55:00  1.20317  1.20302      118  \n",
       "2018-01-05 21:56:00  1.20326  1.20307      184  \n",
       "2018-01-05 21:57:00  1.20319  1.20312       58  \n",
       "2018-01-05 21:58:00  1.20329  1.20314       51  \n",
       "2018-01-05 21:59:00  1.20363  1.20327       14  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_candles('EUR/USD', period='m1', number=5)  # five one-minute bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Time Windows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, one can specifiy `start` and `stop` values to specifiy the time window for data retrieval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
       "      <th>askopen</th>\n",
       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-16 21:00:00</th>\n",
       "      <td>1.14642</td>\n",
       "      <td>1.14721</td>\n",
       "      <td>1.14723</td>\n",
       "      <td>1.14597</td>\n",
       "      <td>1.14702</td>\n",
       "      <td>1.14761</td>\n",
       "      <td>1.14782</td>\n",
       "      <td>1.14686</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-17 21:00:00</th>\n",
       "      <td>1.14721</td>\n",
       "      <td>1.14768</td>\n",
       "      <td>1.14859</td>\n",
       "      <td>1.14336</td>\n",
       "      <td>1.14761</td>\n",
       "      <td>1.14803</td>\n",
       "      <td>1.14885</td>\n",
       "      <td>1.14360</td>\n",
       "      <td>210489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-18 21:00:00</th>\n",
       "      <td>1.14768</td>\n",
       "      <td>1.15514</td>\n",
       "      <td>1.15821</td>\n",
       "      <td>1.14703</td>\n",
       "      <td>1.14803</td>\n",
       "      <td>1.15549</td>\n",
       "      <td>1.15846</td>\n",
       "      <td>1.14728</td>\n",
       "      <td>347712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-19 21:00:00</th>\n",
       "      <td>1.15514</td>\n",
       "      <td>1.15134</td>\n",
       "      <td>1.15552</td>\n",
       "      <td>1.15089</td>\n",
       "      <td>1.15549</td>\n",
       "      <td>1.15171</td>\n",
       "      <td>1.15603</td>\n",
       "      <td>1.15111</td>\n",
       "      <td>227875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 21:00:00</th>\n",
       "      <td>1.15134</td>\n",
       "      <td>1.16286</td>\n",
       "      <td>1.16575</td>\n",
       "      <td>1.14781</td>\n",
       "      <td>1.15171</td>\n",
       "      <td>1.16325</td>\n",
       "      <td>1.16602</td>\n",
       "      <td>1.14804</td>\n",
       "      <td>422458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-21 21:00:00</th>\n",
       "      <td>1.16286</td>\n",
       "      <td>1.16622</td>\n",
       "      <td>1.16817</td>\n",
       "      <td>1.16181</td>\n",
       "      <td>1.16325</td>\n",
       "      <td>1.16658</td>\n",
       "      <td>1.16843</td>\n",
       "      <td>1.16205</td>\n",
       "      <td>282128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-23 21:00:00</th>\n",
       "      <td>1.16622</td>\n",
       "      <td>1.16588</td>\n",
       "      <td>1.16663</td>\n",
       "      <td>1.16539</td>\n",
       "      <td>1.16658</td>\n",
       "      <td>1.16692</td>\n",
       "      <td>1.16738</td>\n",
       "      <td>1.16641</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-24 21:00:00</th>\n",
       "      <td>1.16588</td>\n",
       "      <td>1.16378</td>\n",
       "      <td>1.16831</td>\n",
       "      <td>1.16245</td>\n",
       "      <td>1.16692</td>\n",
       "      <td>1.16424</td>\n",
       "      <td>1.16855</td>\n",
       "      <td>1.16271</td>\n",
       "      <td>249547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-25 21:00:00</th>\n",
       "      <td>1.16378</td>\n",
       "      <td>1.16442</td>\n",
       "      <td>1.17111</td>\n",
       "      <td>1.16293</td>\n",
       "      <td>1.16424</td>\n",
       "      <td>1.16485</td>\n",
       "      <td>1.17136</td>\n",
       "      <td>1.16320</td>\n",
       "      <td>293787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-26 21:00:00</th>\n",
       "      <td>1.16442</td>\n",
       "      <td>1.17324</td>\n",
       "      <td>1.17388</td>\n",
       "      <td>1.16115</td>\n",
       "      <td>1.16485</td>\n",
       "      <td>1.17376</td>\n",
       "      <td>1.17420</td>\n",
       "      <td>1.16139</td>\n",
       "      <td>363321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-27 21:00:00</th>\n",
       "      <td>1.17324</td>\n",
       "      <td>1.16740</td>\n",
       "      <td>1.17757</td>\n",
       "      <td>1.16490</td>\n",
       "      <td>1.17376</td>\n",
       "      <td>1.16797</td>\n",
       "      <td>1.17782</td>\n",
       "      <td>1.16514</td>\n",
       "      <td>367563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-28 21:00:00</th>\n",
       "      <td>1.16740</td>\n",
       "      <td>1.17481</td>\n",
       "      <td>1.17630</td>\n",
       "      <td>1.16700</td>\n",
       "      <td>1.16797</td>\n",
       "      <td>1.17513</td>\n",
       "      <td>1.17653</td>\n",
       "      <td>1.16724</td>\n",
       "      <td>332212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-30 21:00:00</th>\n",
       "      <td>1.17481</td>\n",
       "      <td>1.17447</td>\n",
       "      <td>1.17504</td>\n",
       "      <td>1.17343</td>\n",
       "      <td>1.17513</td>\n",
       "      <td>1.17523</td>\n",
       "      <td>1.17583</td>\n",
       "      <td>1.17409</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-31 21:00:00</th>\n",
       "      <td>1.17447</td>\n",
       "      <td>1.18403</td>\n",
       "      <td>1.18444</td>\n",
       "      <td>1.17221</td>\n",
       "      <td>1.17523</td>\n",
       "      <td>1.18428</td>\n",
       "      <td>1.18473</td>\n",
       "      <td>1.17244</td>\n",
       "      <td>282104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 21:00:00</th>\n",
       "      <td>1.18403</td>\n",
       "      <td>1.18009</td>\n",
       "      <td>1.18433</td>\n",
       "      <td>1.17839</td>\n",
       "      <td>1.18428</td>\n",
       "      <td>1.18033</td>\n",
       "      <td>1.18464</td>\n",
       "      <td>1.17864</td>\n",
       "      <td>293101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2017-07-16 21:00:00  1.14642   1.14721  1.14723  1.14597  1.14702   1.14761   \n",
       "2017-07-17 21:00:00  1.14721   1.14768  1.14859  1.14336  1.14761   1.14803   \n",
       "2017-07-18 21:00:00  1.14768   1.15514  1.15821  1.14703  1.14803   1.15549   \n",
       "2017-07-19 21:00:00  1.15514   1.15134  1.15552  1.15089  1.15549   1.15171   \n",
       "2017-07-20 21:00:00  1.15134   1.16286  1.16575  1.14781  1.15171   1.16325   \n",
       "2017-07-21 21:00:00  1.16286   1.16622  1.16817  1.16181  1.16325   1.16658   \n",
       "2017-07-23 21:00:00  1.16622   1.16588  1.16663  1.16539  1.16658   1.16692   \n",
       "2017-07-24 21:00:00  1.16588   1.16378  1.16831  1.16245  1.16692   1.16424   \n",
       "2017-07-25 21:00:00  1.16378   1.16442  1.17111  1.16293  1.16424   1.16485   \n",
       "2017-07-26 21:00:00  1.16442   1.17324  1.17388  1.16115  1.16485   1.17376   \n",
       "2017-07-27 21:00:00  1.17324   1.16740  1.17757  1.16490  1.17376   1.16797   \n",
       "2017-07-28 21:00:00  1.16740   1.17481  1.17630  1.16700  1.16797   1.17513   \n",
       "2017-07-30 21:00:00  1.17481   1.17447  1.17504  1.17343  1.17513   1.17523   \n",
       "2017-07-31 21:00:00  1.17447   1.18403  1.18444  1.17221  1.17523   1.18428   \n",
       "2017-08-01 21:00:00  1.18403   1.18009  1.18433  1.17839  1.18428   1.18033   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2017-07-16 21:00:00  1.14782  1.14686      183  \n",
       "2017-07-17 21:00:00  1.14885  1.14360   210489  \n",
       "2017-07-18 21:00:00  1.15846  1.14728   347712  \n",
       "2017-07-19 21:00:00  1.15603  1.15111   227875  \n",
       "2017-07-20 21:00:00  1.16602  1.14804   422458  \n",
       "2017-07-21 21:00:00  1.16843  1.16205   282128  \n",
       "2017-07-23 21:00:00  1.16738  1.16641      184  \n",
       "2017-07-24 21:00:00  1.16855  1.16271   249547  \n",
       "2017-07-25 21:00:00  1.17136  1.16320   293787  \n",
       "2017-07-26 21:00:00  1.17420  1.16139   363321  \n",
       "2017-07-27 21:00:00  1.17782  1.16514   367563  \n",
       "2017-07-28 21:00:00  1.17653  1.16724   332212  \n",
       "2017-07-30 21:00:00  1.17583  1.17409      163  \n",
       "2017-07-31 21:00:00  1.18473  1.17244   282104  \n",
       "2017-08-01 21:00:00  1.18464  1.17864   293101  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = dt.datetime(2017, 7, 15)\n",
    "stop = dt.datetime(2017, 8, 1)\n",
    "con.get_candles('EUR/USD', period='D1',\n",
    "                start=start, stop=stop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python ecosystem provides a number of alternatives to **visualize financial time series data**. The standard plotting library is `matplotlib` (see http://matplotlib.org) which is tighly integrated with `pandas` `DataFrame` objects, allowing for efficient visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pylab import plt\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `columns` parameter, one can specifiy which data columns are returned. Here, just one column is specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = con.get_candles('EUR/USD', period='m1',\n",
    "                       columns=['askopen'], number=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code visualizes the only financial time series in the `DataFrame` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ed9d05be0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5bTNWwLxuO+Go+oCJiIhI8SypAOZ22CbdhijvGpedSDy1QCMSERERmWhJBbDsFOT0Acxq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\npMn+Pws3/fyyE9DMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ee06157b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6), lw=0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specifying the `columns` parameter to be `asks` (`bids`) returns all columns related to ask (bid) prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = con.get_candles('EUR/USD', period='m1',\n",
    "                       columns=['asks'], number=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ed97b16a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xrPdKofSzT3q4OiGEEEJ0BwlmV5Exk6LR8jxIzzlq14cD/t4uxIxMorQJGo9k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\nmImIiIi4xP8DtXjiqubi4vYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ee0615550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6), lw=0.8);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
